Abstract #4078

7T to 3T domain adaptation in white matter lesion segmentation on T2-weighted (T2-w) FLAIR images using deep learning

Jinghang Li¹, Eduardo Diniz², Taylor Forry³, Tamer Ibrahim^1,4, Howard Aizenstein^1,4, and Minjie Wu⁴

¹Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States, ²Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States, ³Temple University, Philadelphia, PA, United States, ⁴Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States

Synopsis

Keywords: High-Field MRI, Machine Learning/Artificial IntelligenceIn this work, we explored the domain adaptation problem in deep learning segmentation. Specifically, we applied the residual U-net [1] on 3T and 7T Fluid Attenuated Inverse Recovery (FLAIR) images to delineate the white matter hyperintensity (WMH) in a 2D fashion. We leveraged learning without forgetting [2] to regulate the network’s learning in the new domain to preserve the model’s performance on the old domain while still achieving satisfying results on the new domain images.

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